The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Memes are topics that spread among people in a community. They could be ideas, behaviors, or styles, etc. Recent research found that the spreading of memes on microblogs displays epidemic patterns, while their popularity is predictable. Aiming for high accuracy, complex models that consider the structural details of community graphs are common. On the contrary, some models simply assume a community...
Infrastructure failures have severe consequences which often have a negative impact on the society and the economy. In this paper, we propose a machine learning model to assist in risk management to minimise the cost of infrastructure maintenance. Due to the vast volume and complexity of infrastructure datasets, such problem is often computationally expensive to compute. A Bayesian nonparametric approach...
This paper presents information visualization methods for revealing relations of multi-attributes in big infrastructure data. The interactive parallel coordinates, sunburst visualization and combinational visualization approaches are used to represent different relations to get insights from the big infrastructure data. The water pipe failure data is used as a case study to show the effectiveness...
While time-series analysis techniques are commonly used in financial forecasting, a key source of market volatility is omitted from these models. Financial news is known to be making persuasive impact to the markets. Without considering these additional signals, only sub-optimal predictions can be made. This paper proposes a supervised topic learning approach to improve portfolio return. It is achieved...
Different ranking algorithms have been proposed to fulfil the need of ranking. The problem is that most of the existing algorithms and models are just applicable on a specific data. When the data is imbalanced and heterogeneous, finding the records belonging to the minority class is significant especially in failure cases. So considering ranking as a classification problem of predicting the specific...
A virtual-metrology-based (VM-based) baseline-predictive-maintenance (BPM) scheme was proposed by the authors recently. By applying the BPM scheme, fault diagnosis and prognosis can be accomplished and the requirement of massive historical failure data can also be released. The accuracy of the BPM scheme highly depends on the correctness of the baseline models in the BPM scheme. The samples of creating...
Accurate short-term wind speed prediction is very important to improve the security and stability of power grid. The method of least squares support vector machine (LS-SVM) for short-term wind speed prediction is proposed in this paper. In order to avoid inaccuracy of parameter selection and improve the accuracy of prediction, genetic algorithm is used to optimize the parameters of LS-SVM. It is proved...
Selection schemes between neural-network (NN) and multiple-regression (MR) outputs of a virtual metrology system (VMS) are studied in this paper. Both NN and MR are applicable algorithms for implementing virtual-metrology (VM) conjecture models. A MR algorithm may achieve better accuracy only with a stable process, whereas a NN algorithm may have superior accuracy when equipment property drift or...
Advanced studies of selection schemes between neural-network (NN) and multiple-regression (MR) outputs of a virtual metrology system (VMS) are presented in this paper. Both NN and MR are applicable algorithms for implementing VM conjecture models. But a MR algorithm may achieve better accuracy only with a stable process, whereas a NN algorithm may has superior accuracy when equipment property drift...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.